Dataset Information

CABRA: Cluster and Annotate Blast Results Algorithm.

ABSTRACT: Basic local alignment search tool (BLAST) searches are frequently used to look for homologous sequences and to annotate a query protein, but the increasing size of protein databases makes it difficult to review all results from a similarity search.We developed a web tool called Cluster and Annotate Blast Results Algorithm (CABRA), which enables a rapid BLAST search in a variety of updated reference proteomes, and provides a new way to functionally evaluate the results by the subsequent clustering of the hits and annotation of the clusters. The tool can be accessed from the following web-resource: http://cbdm-01.zdv.uni-mainz.de/~munoz/CABRA .Cluster and Annotate Blast Results Algorithm simplifies the analysis of the results of a BLAST search by providing an overview of the result's annotations organized in clusters that can be iteratively modified by the user.

SUBMITTER: Mier P

PROVIDER: S-EPMC4851773 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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CABRA: Cluster and Annotate Blast Results Algorithm.

Mier Pablo P Andrade-Navarro Miguel A MA

BMC research notes 20160430

<h4>Background</h4>Basic local alignment search tool (BLAST) searches are frequently used to look for homologous sequences and to annotate a query protein, but the increasing size of protein databases makes it difficult to review all results from a similarity search.<h4>Findings</h4>We developed a web tool called Cluster and Annotate Blast Results Algorithm (CABRA), which enables a rapid BLAST search in a variety of updated reference proteomes, and provides a new way to functionally evaluate the ...[more]

PMID: 27129717

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Project description:A major computational challenge in the genomic era is annotating structure/function to the vast quantities of sequence information that is now available. This problem is illustrated by the fact that most proteins lack comprehensive annotations, even when experimental evidence exists. We previously theorized that embedded-alignment profiles (simply "alignment profiles" hereafter) provide a quantitative method that is capable of relating the structural and functional properties of proteins, as well as their evolutionary relationships. A key feature of alignment profiles lies in the interoperability of data format (e.g., alignment information, physio-chemical information, genomic information, etc.). Indeed, we have demonstrated that the Position Specific Scoring Matrices (PSSMs) are an informative M-dimension that is scored by quantitatively measuring the embedded or unmodified sequence alignments. Moreover, the information obtained from these alignments is informative, and remains so even in the "twilight zone" of sequence similarity (<25% identity). Although our previous embedding strategy was powerful, it suffered from contaminating alignments (embedded AND unmodified) and high computational costs. Herein, we describe the logic and algorithmic process for a heuristic embedding strategy named "Adaptive GDDA-BLAST." Adaptive GDDA-BLAST is, on average, up to 19 times faster than, but has similar sensitivity to our previous method. Further, data are provided to demonstrate the benefits of embedded-alignment measurements in terms of detecting structural homology in highly divergent protein sequences and isolating secondary structural elements of transmembrane and ankyrin-repeat domains. Together, these advances allow further exploration of the embedded alignment data space within sufficiently large data sets to eventually induce relevant statistical inferences. We show that sequence embedding could serve as one of the vehicles for measurement of low-identity alignments and for incorporation thereof into high-performance PSSM-based alignment profiles.

Dataset Information

CABRA: Cluster and Annotate Blast Results Algorithm.

Publications

CABRA: Cluster and Annotate Blast Results Algorithm.

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